In computer image analysis, a common task is the selection of objects-of-interest in a captured image. For example, in computer automated cytology, a typical task is the selection, or segmentation, of cell nuclei from an image containing many cells. This task is often separated into two sub-tasks.
The first sub-task is that of scene segmentation. In this sub-task, the original image is coarsely segmented into regions-of-interest, each containing a single cell-nucleus-of-interest. Each region-of-interest may then be viewed as a new, smaller image to process.
The second sub-task may consist of further segmentation of the cell-nucleus-of-interest within each region-of-interest. The second sub-task is the subject of co-pending PCT Patent Application number PCT/AU99/00231 claiming priority from Australian Provisional Patent Application number PP2786 dated 3 Apr. 1998. The Patent Application is entitled Method of Unsupervised Cell Nuclei Segmentation and describes a method of active contours using a Viterbi search algorithm for unsupervised segmentation of cell nuclei.
The first sub-task of scene segmentation operates on an image containing between zero and many objects-of-interest. The method of the present invention is for the implementation of scene segmentation.
Various techniques exist for identifying and segmenting objects in an image. One method of scene segmentation is described by Betel et al [Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology; British Journal of Radiology; vol. 70; no. 837; September 1997, pp 903–17], which discloses the use of simple Top Hat and Watershed algorithms of mathematical morphology to automatically detect and segment microcalcifications on digitized mammograms. The Betel process is of limited use in cell nuclei segmentation due to insufficient noise immunity and region-growing constraints.